OpenRaiser/Intern-Atlas huggingface dataset:
DeMiST: Detection and Mitigation of Stealthy Analog Hardware Trojans
Venue: arXiv cs.AR 2023
Year: 2023

The global semiconductor supply chain involves design and fabrication at various locations, which leads to multiple security vulnerabilities, e.g., Hardware Trojan (HT) insertion. Although most HTs target digital circuits, HTs can be inserted in analog circuits. Therefore, several techniques have been developed for HT insertions in analog circuits. Capacitance-based Analog Hardware Trojan (AHT) is one of the stealthiest HT that can bypass most existing HT detection techniques because it uses negligible charge accumulation in the capacitor to generate stealthy triggers. To address the charge sharing and accumulation issues, we propose a novel way to detect such capacitance-based AHT in this paper. Secondly, we critically analyzed existing AHTs to highlight their respective limitations. We proposed a stealthier capacitor-based AHT (fortified AHT) that can bypass our novel AHT detection technique by addressing these limitations. Finally, by critically analyzing the proposed fortified AHT and existing AHTs, we developed a robust two-phase framework (DeMiST) in which a synchronous system can mitigate the effects of capacitance-based stealthy AHTs by turning off the triggering capability of AHT. In the first phase, we demonstrate how the synchronous system can avoid the AHT during run-time by controlling the supply voltage of the intermediate combinational circuits. In the second phase, we proposed a supply voltage duty cycle-based validation technique to detect capacitance-based AHTs. Furthermore, DeMiST amplified the switching activity for charge accumulation to such a degree that it can be easily detectable using existing switching activity-based HT detection techniques.
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs and predicting the sentiment label can compromise their predictive performance. Furthermore, the succinct nature of financial news, often devoid of sufficient context, can significantly diminish the reliability of LLMs' sentiment analysis. To address these challenges, we introduce a retrieval-augmented LLMs framework for financial sentiment analysis. This framework includes an instruction-tuned LLMs module, which ensures LLMs behave as predictors of sentiment labels, and a retrieval-augmentation module which retrieves additional context from reliable external sources. Benchmarked against traditional models and LLMs like ChatGPT and LLaMA, our approach achieves 15\% to 48\% performance gain in accuracy and F1 score.
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Venue: arXiv cs.LG 2023
Year: 2023

Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multi-view data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the inter-class and intra-class structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD) for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning.Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering.JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.
Expressiveness Results for Timed Modal Mu-Calculi
Venue: arXiv cs.LO 2023
Year: 2023

This paper establishes relative expressiveness results for several modal mu-calculi interpreted over timed automata. These mu-calculi combine modalities for expressing passage of (real) time with a general framework for defining formulas recursively; several variants have been proposed in the literature. We show that one logic, which we call $L^{rel}_{ν,μ}$, is strictly more expressive than the other mu-calculi considered. It is also more expressive than the temporal logic TCTL, while the other mu-calculi are incomparable with TCTL in the setting of general timed automata.
Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms and Legal Aspects
Venue: arXiv cs.SI 2023
Year: 2023

Many researchers and organizations, such as WHO and UNICEF, have raised awareness of the dangers of advertisements targeted at children. While most existing laws only regulate ads on television that may reach children, lawmakers have been working on extending regulations to online advertising and, for example, forbid (e.g., the DSA) or restrict (e.g., the COPPA) advertising based on profiling to children. At first sight, ad platforms such as Google seem to protect children by not allowing advertisers to target their ads to users who are less than 18 years old. However, this paper shows that other targeting features can be exploited to reach children. For example, on YouTube, advertisers can target their ads to users watching a particular video through placement-based targeting, a form of contextual targeting. Hence, advertisers can target children by placing their ads in children-focused videos. Through a series of ad experiments, we show that placement-based targeting is possible on children-focused videos and enables marketing to children. In addition, our ad experiments show that advertisers can use targeting based on profiling (e.g., interest, location, behavior) in combination with placement-based advertising on children-focused videos. We discuss the lawfulness of these two practices concerning DSA and COPPA. Finally, we investigate to which extent real-world advertisers are employing placement-based targeting to reach children with ads on YouTube. We propose a measurement methodology consisting of building a Chrome extension to capture ads and instrument six browser profiles to watch children-focused videos. Our results show that 7% of ads that appear in the children-focused videos we test use placement-based targeting. Hence, targeting children with ads on YouTube is not only hypothetically possible but also occurs in practice...
VI-Diff: Unpaired Visible-Infrared Translation Diffusion Model for Single Modality Labeled Visible-Infrared Person Re-identification
Venue: arXiv cs.CV 2023
Year: 2023

Visible-Infrared person re-identification (VI-ReID) in real-world scenarios poses a significant challenge due to the high cost of cross-modality data annotation. Different sensing cameras, such as RGB/IR cameras for good/poor lighting conditions, make it costly and error-prone to identify the same person across modalities. To overcome this, we explore the use of single-modality labeled data for the VI-ReID task, which is more cost-effective and practical. By labeling pedestrians in only one modality (e.g., visible images) and retrieving in another modality (e.g., infrared images), we aim to create a training set containing both originally labeled and modality-translated data using unpaired image-to-image translation techniques. In this paper, we propose VI-Diff, a diffusion model that effectively addresses the task of Visible-Infrared person image translation. Through comprehensive experiments, we demonstrate that VI-Diff outperforms existing diffusion and GAN models, making it a promising solution for VI-ReID with single-modality labeled data. Our approach can be a promising solution to the VI-ReID task with single-modality labeled data and serves as a good starting point for future study. Code will be available.
Entropic Score metric: Decoupling Topology and Size in Training-free NAS
Venue: arXiv cs.CV 2023
Year: 2023

Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and computational resources to generate accurate models. To overcome these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations; ii) a cyclic search algorithm to separately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet classification.
Reviving Meltdown 3a
Venue: arXiv cs.CR 2023
Year: 2023

Since the initial discovery of Meltdown and Spectre in 2017, different variants of these attacks have been discovered. One often overlooked variant is Meltdown 3a, also known as Meltdown-CPL-REG. Even though Meltdown-CPL-REG was initially discovered in 2018, the available information regarding the vulnerability is still sparse. In this paper, we analyze Meltdown-CPL-REG on 19 different CPUs from different vendors using an automated tool. We observe that the impact is more diverse than documented and differs from CPU to CPU. Surprisingly, while the newest Intel CPUs do not seem affected by Meltdown-CPL-REG, the newest available AMD CPUs (Zen3+) are still affected by the vulnerability. Furthermore, given our attack primitive CounterLeak, we show that besides up-to-date patches, Meltdown-CPL-REG can still be exploited as we reenable performance-counter-based attacks on cryptographic algorithms, break KASLR, and mount Spectre attacks. Although Meltdown-CPL-REG is not as powerful as other transient-execution attacks, its attack surface should not be underestimated.
Written and spoken corpus of real and fake social media postings about COVID-19
Venue: arXiv cs.CL 2023
Year: 2023

This study investigates the linguistic traits of fake news and real news. There are two parts to this study: text data and speech data. The text data for this study consisted of 6420 COVID-19 related tweets re-filtered from Patwa et al. (2021). After cleaning, the dataset contained 3049 tweets, with 2161 labeled as 'real' and 888 as 'fake'. The speech data for this study was collected from TikTok, focusing on COVID-19 related videos. Research assistants fact-checked each video's content using credible sources and labeled them as 'Real', 'Fake', or 'Questionable', resulting in a dataset of 91 real entries and 109 fake entries from 200 TikTok videos with a total word count of 53,710 words. The data was analysed using the Linguistic Inquiry and Word Count (LIWC) software to detect patterns in linguistic data. The results indicate a set of linguistic features that distinguish fake news from real news in both written and speech data. This offers valuable insights into the role of language in shaping trust, social media interactions, and the propagation of fake news.
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
Venue: arXiv cs.LG 2023
Year: 2023

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. This is done by considering software selection approaches, as well as in terms of the technical perspective of AutoML. The requirements for the framework selection are divided into hard and soft criteria regarding their software and ML attributes. Additionally, a classification of AutoML frameworks is made into high- and low-level types, inspired by the findings of. Finally, we select Ray and AutoGluon as the suitable low- and high-level frameworks respectively, as they fulfil all requirements sufficiently and received the best evaluation feedback during the use-case study. Based on those findings, we build an extended Automated Quantum Machine Learning (AutoQML) framework with QC-specific pipeline steps and decision characteristics for hardware and software constraints.
Detecting Semantic Conflicts using Static Analysis
Venue: arXiv cs.SE 2023
Year: 2023

Version control system tools empower developers to independently work on their development tasks. These tools also facilitate the integration of changes through merging operations, and report textual conflicts. However, when developers integrate their changes, they might encounter other types of conflicts that are not detected by current merge tools. In this paper, we focus on dynamic semantic conflicts, which occur when merging reports no textual conflicts but results in undesired interference - causing unexpected program behavior at runtime. To address this issue, we propose a technique that explores the use of static analysis to detect interference when merging contributions from two developers. We evaluate our technique using a dataset of 99 experimental units extracted from merge scenarios. The results provide evidence that our technique presents significant interference detection capability. It outperforms, in terms of F1 score and recall, previous methods that rely on dynamic analysis for detecting semantic conflicts, but these show better precision. Our technique precision is comparable to the ones observed in other studies that also leverage static analysis or use theorem proving techniques to detect semantic conflicts, albeit with significantly improved overall performance.
From task structures to world models: What do LLMs know?
Venue: arXiv cs.AI 2023
Year: 2023

In what sense does a large language model have knowledge? The answer to this question extends beyond the capabilities of a particular AI system, and challenges our assumptions about the nature of knowledge and intelligence. We answer by granting LLMs "instrumental knowledge"; knowledge defined by a certain set of abilities. We then ask how such knowledge is related to the more ordinary, "worldly" knowledge exhibited by human agents, and explore this in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge, and suggest such recovery will be governed by an implicit, resource-rational tradeoff between world models and task demands.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Venue: arXiv cs.LG 2023
Year: 2023

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks.
A Serverless Architecture for Efficient and Scalable Monte Carlo Markov Chain Computation
Venue: arXiv cs.DC 2023
Year: 2023

Computer power is a constantly increasing demand in scientific data analyses, in particular when Markov Chain Monte Carlo (MCMC) methods are involved, for example for estimating integral functions or Bayesian posterior probabilities. In this paper, we describe the benefits of a parallel computation of MCMC using a cloud-based, serverless architecture: first, the computation time can be spread over thousands of processes, hence greatly reducing the time the user should wait to have its computation completed. Second, the overhead time required for running in parallel several processes is minor and grows logarithmically with respect to the number of processes. Third, the serverless approach does not require time-consuming efforts for maintaining and updating the computing infrastructure when/if the number of walkers increases or for adapting the code to optimally use the infrastructure. The benefits are illustrated with the computation of the posterior probability distribution of a real astronomical analysis.
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection
Venue: arXiv cs.CL 2023
Year: 2023

The detection of Alzheimer's disease (AD) from spontaneous speech has attracted increasing attention while the sparsity of training data remains an important issue. This paper handles the issue by knowledge transfer, specifically from both speech-generic and depression-specific knowledge. The paper first studies sequential knowledge transfer from generic foundation models pretrained on large amounts of speech and text data. A block-wise analysis is performed for AD diagnosis based on the representations extracted from different intermediate blocks of different foundation models. Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD. A parallel knowledge transfer framework is studied that jointly learns the information shared between these two tasks. Experimental results show that the proposed method improves AD and depression detection, and produces a state-of-the-art F1 score of 0.928 for AD diagnosis on the commonly used ADReSSo dataset.
Enhanced Backpressure Routing Using Wireless Link Features
Venue: arXiv cs.NI 2023
Year: 2023

Backpressure (BP) routing is a well-established framework for distributed routing and scheduling in wireless multi-hop networks. However, the basic BP scheme suffers from poor end-to-end delay due to the drawbacks of slow startup, random walk, and the last packet problem. Biased BP with shortest path awareness can address the first two drawbacks, and sojourn time-based backlog metrics were proposed for the last packet problem. Furthermore, these BP variations require no additional signaling overhead in each time step compared to the basic BP. In this work, we further address three long-standing challenges associated with the aforementioned low-cost BP variations, including optimal scaling of the biases, bias maintenance under mobility, and incorporating sojourn time awareness into biased BP. Our analysis and experimental results show that proper scaling of biases can be achieved with the help of common link features, which can effectively reduce end-to-end delay of BP by mitigating the random walk of packets under low-to-medium traffic, including the last packet scenario. In addition, our low-overhead bias maintenance scheme is shown to be effective under mobility, and our bio-inspired sojourn time-aware backlog metric is demonstrated to be more efficient and effective for the last packet problem than existing approaches when incorporated into biased BP.
Context-aware Trace Contracts
Venue: arXiv cs.LO 2023
Year: 2023

The behavior of concurrent, asynchronous procedures depends in general on the call context, because of the global protocol that governs scheduling. This context cannot be specified with the state-based Hoare-style contracts common in deductive verification. Recent work generalized state-based to trace contracts, which permit to specify the internal behavior of a procedure, such as calls or state changes, but not its call context. In this article we propose a program logic of context-aware trace contracts for specifying global behavior of asynchronous programs. We also provide a sound proof system that addresses two challenges: To observe the program state not merely at the end points of a procedure, we introduce the novel concept of an observation quantifier. And to combat combinatorial explosion of possible call sequences of procedures, we transfer Liskov's principle of behavioral subtyping to the analysis of asynchronous procedures.